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Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Using Neuro-fuzzy Technique

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Automated fruit grading in local fruit industries are gradually receiving attention as the use of technology in upgrading the quality of food products are now acknowledged. In this paper, outer surface colors of palm oil fresh fruit bunches (FFB) are analyzed to automatically grade the fruits into over ripe, ripe and unripe. We compared two methods of color grading: 1) using RGB digital numbers and 2) colors classifications trained using a supervised learning Hebb technique and graded using fuzzy logic. A total of 90 images are used as the training images and 45 images are tested in the grading process. Overall, automated grading using RGB digital numbers produced an average of 49% success rate, while the neuro-fuzzy approach achieved an accuracy level of 73.3%.
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... Inductive type was introduced by Harun et al. [16] that utilized the frequency variations to measure the maturity of oil palm fruit bunches. The sensitivity of the inductive concept was improved from the above research in [17] and a dual flat-type shape of the air coil sensor was introduced in [18]. Jamil et al. [19] and Fadilah et al. [20] utilized a neural network and the fuzzy technique to classify the ripe oil palm fruit as further development in this area. ...
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... Non-optical methods usually measure the basic molecular content of all fruits such as moisture, which influences the overall performance www.nature.com/scientificreports/ of the system. The optical detection method can be further subdivided into 3 groups: imaging 5,[17][18][19][20][21][21][22][23][24][25][26] , spectroscopy 6,[27][28][29][30] and spectral imaging [31][32][33][34][35] . Among all the methods introduced earlier, imaging is the most popular method to date due to its portability, low cost, and ease of implementation. ...
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... For instance, Alfatni et al. [21] used the mean value of the red, green, and blue channels of palm oil FFB images as features with a simple rule-based algorithm for classification. The features were also utilized by Jamil et al. [22], but with a neuro-fuzzy algorithm for classification. Similarly, Fadilah et al. [23] used simple color features for classification, in this case, selected hue values from all pixels in the palm oil FFB image. ...
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